Breast tumors are from the common infections among women around the world. Classifying the various types of breast tumors contribute to treating breast tumors more efficiently. However, this classification task is often hindered by dense tissue patterns captured in mammograms. The present study has been proposed a dense tissue pattern characterization framework using deep neural network. A total of 322 mammograms belonging to the mini-MIAS dataset and 4880 mammograms from DDSM dataset have been taken, and an ROI of fixed size 224 × 224 pixels from each mammogram has been extracted. In this work, tedious experimentation has been executed using different combinations of training and testing sets using different activation function withAlexNet,ResNet-18model. Data augmentation has been used to create a similar type of virtual image for proper training of the DL model. After that, the testing set is applied on the trained model to validate the proposed model. During experiments, four different activation functions ‘sigmoid’, ‘tanh’, ‘ReLu’, and ‘leakyReLu’ are used, and the outcome for each function has been reported. It has been found that activation function ‘ReLu’ perform always outstanding with respect to others. For each experiment, classification accuracy and kappa coefficient have been computed. The obtained accuracy and kappa value for MIAS dataset usingResNet-18model is 91.3% and 0.803, respectively. For DDSM dataset, the accuracy of 92.3% and kappa coefficient value of 0.846 are achieved. After the combination of both dataset images, the achieved accuracy is 91.9%, and kappa coefficient value is 0.839 usingResNet-18model. Finally, it has been concluded that theResNet-18model andReLuactivation function yield outstanding performance for the task.

The Z‐source inverter is one of the most interesting power electronic converters. Over the past decades, research and development have been in high demand to meet the challenges for improving the converter performance with reduced weight and cost while having high efficiency along with high gain or boost factor. Since the inception, tons of resolutions, customizations, and improvements have appeared to achieve these performance goals. To overcome the most common drawback, i.e. the low boost factor of classical Z‐source inverters, the switched inductor/capacitor and switched boost Z‐source inverter topologies have become predominant in the recent epoch. Furthermore, the higher component voltage stress, discontinuous input current, and high inrush start‐up current issues have also been addressed and solved in various literature as part of this topical advancement. In this study, a comprehensive review of various switched impedance network‐based Z‐source inverter topologies has been accomplished for providing a quick report to researchers and engineers so that they can consider this as a one‐stop solution for selecting inverters from this group. The overall study covers the methodology, features, and relevant important results of the latest switched Z‐source inverter models in order to demonstrate the coordination between pitfalls and improvements.

Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes’ energy consumption data. From the literature, it has been identified that the data imputation with machine learning (ML)-based single-classifier approaches are used to address data quality issues. However, these approaches are not effective to address the hidden issues of smart home energy consumption data due to the presence of a variety of anomalies. Hence, this paper proposes ML-based ensemble classifiers using random forest (RF), support vector machine (SVM), decision tree (DT), naive Bayes, K-nearest neighbor, and neural networks to handle all the possible anomalies in smart home energy consumption data. The proposed approach initially identifies all anomalies and removes them, and then imputes this removed/missing information. The entire implementation consists of four parts. Part 1 presents anomaly detection and removal, part 2 presents data imputation, part 3 presents single-classifier approaches, and part 4 presents ensemble classifiers approaches. To assess the classifiers’ performance, various metrics, namely, accuracy, precision, recall/sensitivity, specificity, and F1 score are computed. From these metrics, it is identified that the ensemble classifier “RF+SVM+DT” has shown superior performance over the conventional single classifiers as well the other ensemble classifiers for anomaly handling.

Innovations in electric vehicle technology have led to a need for maximum energy storage in the energy source to provide some extra kilometers. The size of electric vehicles limits the size of the batteries, thus limiting the amount of energy that can be stored. Range anxiety amongst the crowd prevents the entire population from shifting to a completely electric mode of transport. The extra energy harnessed from the kinetic energy produced due to braking during deceleration is sent back to the batteries to charge them, a process known as regenerative braking, providing a longer range to the vehicle. The work proposes efficient machine learning-based methods used to harness maximum braking energy from an electric vehicle to provide longer mileage. The methods are compared to the energy harnessed using fuzzy logic and artificial neural network techniques. These techniques take into consideration the state of charge (SOC) estimation of the battery, or the supercapacitor and the brake demand, to calculate the energy harnessed from the braking power. With the proposed machine learning techniques, there has been a 59% increase in energy extraction compared to fuzzy logic and artificial neural network methods used for regenerative energy extraction.

COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37% when evaluated using a set of 470 lung CT images.

Understanding protein subcellular localization is vital and indispensable in proteomics research. Molecular biology and computer science developments have enabled the use of computational approaches to identify proteins in cells. An excellent method for locating proteins is confocal microscopy, used by the Human Protein Atlas (HPA). By categorizing human proteins, it can assist researchers in better comprehending human pathophysiology and assist doctors in automating medical image interpretation. Human protein Atlas comprises millions of images annotated with single or multiple labels. However, only a few methods for automated prediction of protein localization have been developed, and they mostly concentrate on single-label classification. Therefore, a recognition system for multi-label classification of HPA with acceptable performance should be developed. Hence, this study aims to develop a deep learning-based system for the multi-label classification of HPA. Specifically, two architectures have been proposed in this work for automatically extracting features from the images and predicting the localization of the proteins in 28 subcellular compartments. First, a convolutional neural network has been proposed, which has been trained from scratch and second an ensemble-based model using transfer learning architectures has been proposed. The results demonstrate that both models are effective in classifying proteins according to their location in the major cellular organelles. Yet, in this study, the proposed convolutional network outperforms the ensemble model in classification of images with multiple simultaneous protein localizations. Three performance metrics standards—recall, accuracy, and f1-score—were used to assess the models. The proposed convolutional neural network beats the ensemble model by achieving recall of 0.75, precision of 0.75 and f1-score of 0.74.